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Comparative Evaluation of Prediction Model between Inference Fuzzy System and Universal Kriging for Spatial Data

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  • Ghanim Mahmood Dhaher

Abstract

This paper dealt with one of the spatial interpolation methods in the geostatistics field. The purpose of this research is to get the parameters of unbiased estimators based on regionalized random variables in spatial statistics. In this paper, we used universal kriging with the fuzzy inference system by the Mamdani technique. the objective of this work is to estimate the parameters of covariance functions relying on spatial real for the depth of groundwater in Mosul city, Iraq. The data adopted contains (100) real data with locations representing the depth. From the results we show the best model with the constructs of weights, we illustrate the performance of universal kriging is the best when corresponding with the fuzzy system. In conclusion, the improvement of any method of spatial interpolation or fuzzy system does not depend on more statistical structures but depends on the efficiency of the method which satisfies the conditions of weights and minimum variance errors. All programming is applied by Matlab language.

Suggested Citation

  • Ghanim Mahmood Dhaher, 2022. "Comparative Evaluation of Prediction Model between Inference Fuzzy System and Universal Kriging for Spatial Data," Technium, Technium Science, vol. 4(1), pages 115-125.
    • Handle: RePEc:tec:techni:v:4:y:2022:i:1:p:115-125
    DOI: 10.47577/technium.v4i10.7824
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    References listed on IDEAS

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    1. Ramón Giraldo & Luis Herrera & Víctor Leiva, 2020. "Cokriging Prediction Using as Secondary Variable a Functional Random Field with Application in Environmental Pollution," Mathematics, MDPI, vol. 8(8), pages 1-13, August.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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